Arabian Journal for Science and Engineering最新文献

The use of cryogenic distillation for separating olefins and paraffin is an energy-intensive process due to the need for large columns and multiple trays. Recent innovations in non-thermal techniques, such as membrane separation, aim to reduce energy consumption. This study compares membrane separation and cryogenic distillation for separating propane/propylene mixtures. Multi-objective optimization technique was used to identify the membrane with the best separation performance from over 100 polymeric membrane samples. The data collection process utilized a 50:50 volume mixed gas composition to simulate real-life industrial scenarios. The separation performance of the membrane and cryogenic distillation units were modeled and simulated using Aspen Plus, Aspen HYSYS, and Aspen Custom Modeler. This was followed by a comparative analysis using process intensification (PI) metrics integrated into the digitally modified logic method. The study revealed that membrane separation is superior to cryogenic distillation in terms of productivity by weight with installation, flexibility (temperature, pressure, number of equipment), production purity, rejection purity, and modularity. In contrast, distillation was observed to outperform membrane only in mass and waste intensity, which was expected due to the separation mechanism of the distillation. Overall, membrane separation was preferred in 68% of the PI metrics, while distillation was favored in 32%. Therefore, based on these PI metrics, membrane separation was found to be more efficient in separating propane/propylene mixtures when compared to cryogenic distillation.

In vehicle-to-vehicle (V2V) networks, vegetation has a significant attenuation impact on radio signal propagation; however, existing research either lacks relevance to V2V environments or requires additional experimental and modeling studies to provide extensive insights into propagation patterns for different scenarios. This study focused on a comprehensive investigation of the varying impacts of vegetation density on V2V communication on flat or sloped roads. Therefore, experimental measurements were conducted in different vegetation densities and road types. Path-loss modeling showed that the log-normal model suits vegetation areas, while for no-vegetation areas, it fits flat roads, and the two-ray model is better for sloped roads. Then, the vegetation density-based empirical V2V attenuation model was developed for each of the flat and sloped roads, incorporating both distance and success rate parameters. The results reveal that the proposed model has been quite successful in determining the required fading depths to achieve the desired success rates, with an average R(^2) of 0.9 and an RMSE of less than 2 dB, calculated using regression analysis and validated with independent test data. Finally, the proposed models were validated using independent V2V test data, achieving RMSE reductions of 2.1%-45.4% compared to existing literature models and demonstrating superior accuracy in predicting vegetation-based attenuation. This study fills a critical gap in the literature as the first comprehensive examination of V2V communication in the context of combining different vegetation density environments with various road types.

Federated semantic segmentation (FSS) has emerged as a promising solution for collaborative model training across distributed devices while preserving data privacy. However, current FSS frameworks fundamentally require homogeneous semantic segmentation labels across all clients, posing key challenges for cross-institutional collaboration with heterogeneous type label(semantic/instance/panoptic segmentation) across organizations. To bridge this critical gap, we propose a novel FSS framework that enables collaborative learning across multiple label types within a unified architecture, named as FedMTL. To address noise in pseudo-labeling, we propose a confident pseudo-label generation algorithm that rectifies region-level semantics through dominant category distribution analysis, achieving instance-guided adaptive label refinement. The proposed adaptive weight aggregation strategy resolves client contribution imbalance by foreground-density-aware weighting, where local models with elevated semantic saliency receive prioritized coefficients to enhance discriminative feature propagation. Experimental validation on Pascal VOC and Cityscapes demonstrates FedMTL’s superior performance, with maximum improvements of +3.1% mIoU on Pascal VOC and +2.7% mIoU on Cityscapes over FedAvg.

In this study, two novel methanesulfonic acid (MSA)-based deep eutectic solvents (DESs) were developed, namely (MSA)/tetraoctylammonium bromide (MSA/TOAB) and MSA/choline chloride (MSA/ChCl), offering a breakthrough in green catalysis for biodiesel production. These DESs demonstrated exceptional catalytic activity, achieving over 99% free fatty acid (FFA) conversion under optimized conditions: a methanol-to-oleic acid (M/O) molar ratio of 12:1, a DES dosage of 2 wt.%, a reaction temperature of 70°C, and a reaction time of 100 min for MSA/TOAB and 60 min for MSA/ChCl. Their industrial applicability was validated by processing high-acid value feedstocks (e.g., grease trap waste and palm acid oil), effectively reducing acid values to below 1 mg KOH/g with minimal pretreatment. Kinetic analysis revealed low activation energies (10.85 kJ/mol for MSA/TOAB and 6.59 kJ/mol for MSA/ChCl), demonstrating their superior energy efficiency. The recyclability of both DESs was also evaluated, with MSA/ChCl retaining over 99% efficiency after three cycles, highlighting its superior stability. Following the esterification, transesterification was successfully conducted using KOH as a catalyst, achieving fatty acid methyl ester (FAME) yields between 87.71 and 96.70%. The highest yield (96.70%) was obtained from soybean oil/oleic acid treated with MSA/ChCl, demonstrating the effectiveness of DESs in ensuring high biodiesel conversion by minimizing FFAs and reducing soap formation. These MSA-based DESs present highly efficient, greener alternatives to conventional acid catalysts, offering advantages such as lower corrosiveness, easy separation, and milder reaction conditions. This study introduces a novel, scalable, and sustainable approach to biodiesel production, emphasizing the potential of MSA-based DESs as next-generation catalysts for industrial applications.

This study developed and evaluated novel cellulose nanofibers (CNFs) and cinnamon-modified CNFs (Cin@CNFs) derived from oil palm frond waste for the removal of ibuprofen (IBP) and paracetamol (PC) from aqueous solutions, addressing global water scarcity and pharmaceutical contamination. The developed materials were characterized using different techniques including SEM with EDX, FTIR, and BET analysis before and after adsorption process. The results showed the adsorption of IBP and PC onto CNFs is characteristic of fast rate, 5 and 15 min, respectively. The maximum adsorption capacities of CNFs toward IBP and PC are 38.714 and 28.2 mg/g, respectively, while cinnamon@CNFs recorded maximum adsorption capacities of 12.1 and 24.69 mg/g toward IBP and PC, respectively. The kinetic modeling investigations for adsorption of IBP onto CNFs showed that the obtained experimental data are better fitted with the pseudo-first-order model, pseudo-second-order model, and mixed first and second model, while intraparticle diffusion is the best fit to describe adsorption of PC. Isotherm models were evaluated for modeling. Out of different models, Langmuir–Freundlich isotherm model is the best to describe IBP onto CNFs adsorption system; Baudu and Sips models are the best to describe PC onto CNFs adsorption system. For the adsorption of PC onto Cin@CNFs, Langmuir–Freundlich and Baudu models are the best. For IBP adsorption onto Cin@CNFs, Freundlich model can be used to describe this adsorption system. Both of CNFs and Cin@CNFs can be concluded to be a prominent and efficient adsorbent for IBP and PC contributing to sustainable water management.

This study re-examines the role of calcium ions (({Ca}^{2+})) in pyrite flocculation using acrylamide-based flocculants (A26 and A27), combining experimental and molecular dynamics (MD) approaches. Contrary to conventional wisdom, results demonstrate that ({Ca}^{2+}) adversely influences flocculation efficiency. Laboratory tests showed that increasing ({Ca}^{2+}) concentrations (up to 150 mg/L) reduced settling velocities by up to 50% and increased turbidity, with the high-acrylamide flocculant A27 being particularly affected. MD simulations revealed that ({Ca}^{2+}) neutralizes negative charges on both pyrite surfaces and flocculant polymers, weakening critical hydrogen bonding and electrostatic interactions. This disruption caused a 2 Å shift in flocculant adsorption position and decreased floc density by 15–20%, leading to less stable aggregates. Performance depended strongly on flocculant composition: A27 (17:1 acrylamide: acrylic acid ratio) outperformed A26 (9:1 ratio) due to enhanced hydrogen bonding, but both suffered efficiency losses with ({Ca}^{2+}). Optimal flocculation occurred at pH 10.5 without ({Ca}^{2+}), where A27 achieved 142.07 m/h settling velocity. FTIR analysis confirmed electrostatic interactions dominated the adsorption mechanism, with no evidence of ({Ca}^{2+}) bridging. These findings challenge established paradigms about ({Ca}^{2+})’s beneficial role and provide molecular-level insights for optimizing flocculant design in mineral processing, particularly for ({Ca}^{2+})-rich systems. The study highlights the need to reconsider water treatment strategies in mining operations where calcium concentrations may compromise flocculation performance.

Hydraulic fracturing is an important stimulation measure for tight sandstone oil reservoirs. CO₂, as an effective energizing gas and displacement agent, can improve hydraulic fracturing and oil production performance. A comprehensive model capable of simulating hydraulic fracturing and oil production as a continuous process is valuable for evaluating the role of CO₂ in different stages. In this paper, a novel integrated mechanism model was established to simulate fracturing and production. The model incorporates mechanisms such as fracture opening and closing, fracturing fluid imbibition, CO₂–crude oil interaction, and asphaltene deposition damage. These mechanisms were further characterized and coupled in the numerical model developed by the software CMG-STARS to conduct simulation works. To verify the model’s reliability, a sensitivity analysis was carried out based on a single fracture to assess the influence of various factors on fracturing and EOR performance. The simulation results indicate that hydraulic fracturing, CO₂ energization, CO₂ huff and puff, and fracturing fluid imbibition can enhance oil recovery (EOR), except for asphaltene deposition. CO₂-energized fracturing followed by depletion production requires less CO₂ injection as a front slug and results in longer fractures, a wider imbibition area, and lower asphaltene deposition risk. Under the basic conditions, the EOR factors contributed by hydraulic fracturing, CO₂ energization, fluid imbibition and asphaltene deposition are 23.06%, 3.92%, 3.44%, and  −1.25%, respectively, and the CO₂ cannot be stored effectively. In contrast, conventional fracturing followed by CO₂ huff and puff requires more CO₂ with a storage efficiency of 44.4%, but results in poorer hydraulic fracturing performance, general CO₂ huff and puff, a smaller imbibition area, and more severe asphaltene deposition damage. The corresponding EOR factors are 17.60%, 3.97%, 2.47%, and -2.22%, respectively. This model can be applied to optimize fracturing and production parameters, providing deeper insights into the fracturing stimulation process.

Reservoir petrophysical characterization stands as an essential initial step in petroleum production and gas storage operations. It involves the use of scientific and engineering tools to understand and explore the nature of the reservoir formation, its fluid content, and the most effective and efficient way of producing it. This involves determining the wetting behavior (wettability), the pore storage capacity (porosity), the quantity of individual fluids (saturation), and the ability of the reservoir to deliver its fluid to the wellbore (permeability). Conventional methods for determining these petrophysical properties such as the special core analysis laboratory (SCAL) and geophysical/petrophysical logs are being practiced. However, traditional SCAL, seismic, and logging methods are time-consuming and costly. Machine learning techniques are faster and help in analysis and better understanding of SCAL and logging methods, and it also provides reliable estimations of reservoir petrophysical properties. Therefore, this review provides a comprehensive overview of recent advancements in machine learning (ML) applied to reservoir petrophysics, covering applications in hydrocarbon exploration, enhanced recovery, and carbon dioxide (CO₂) and hydrogen (H₂) storage. Techniques for reservoir petrophysical characterization are explored, focusing on ML applications in rock typing, porosity/permeability estimation, fluid identification, and wettability assessment. Challenges and limitations associated with ML algorithms in petrophysical analyses are discussed, with insights into future research directions. The review encompasses a broad range of ML algorithms such as artificial neural networks, support vector machines, decision trees, and ensemble methods. Structured sections discuss ML-based petrophysical characterization, ML in CO₂/H₂ storage, integrated workflows combining ML with traditional methods, and challenges of ML applications in petrophysics. The review aims to illuminate the transformative impact of ML on reservoir petrophysics and its potential in CO₂ and H₂ storage, offering valuable insights for researchers and industry professionals. Promising results have been achieved with ML in predicting petrophysical properties, lithology classification, and fluid identification. Opportunities for further research and development in ML applications for reservoir petrophysics are identified, emphasizing the integration of ML with physics-informed models and conventional analysis methods. This review uniquely covers both laboratory and field data, making it a comprehensive resource for understanding ML techniques in reservoir petrophysics, spanning oil and gas reservoirs as well as CO₂ and H₂ subsurface storage operations.

Due to the increasing reliance of cellular devices on the limited licensed spectrum, mobile network operators are exploring the potential of the extensive unlicensed spectrum. This paper addresses the critical issue of managing the coexistence of 5G and Wi-Fi 6 networks. It introduces a novel coexistence technique, called call admission control (CAC), which admits users based on their bit rate, thereby achieving a balanced distribution for 5G and Wi-Fi 6. The simulation results demonstrate that while coexistence can impact 5G performance–as 5G latency is always better–our proposed model effectively balances the network load, improving network performance by ~ 127% better throughput and ~ 68% better capacity. After applying CAC coexistence, an algorithm for bandwidth reservation is illustrated to determine which services (VoIP or video) will be admitted. Through comprehensive simulations, our study observed that the CAC mechanism reduces the blocking probability of video calls by 97.4% and improves throughput by an average of 48.5% compared to the duty cycle mechanism. Our results indicate that CAC significantly reduces interference and enhances overall network efficiency, providing more stable and reliable communication experience due to very low latency (1 ms) and low blocking probability (0.2%). This study ensures the potential of CAC as a viable strategy for mitigating coexistence issues in next-generation wireless networks, as the conventional CAC is a single-stage process applied to a single network, whereas our proposed system employs a multi-stage CAC approach for efficient coexistence between 5G and Wi-Fi 6, to study its impact on key performance metrics.

Metallic bipolar plates in proton exchange membrane fuel cells (PEMFCs) offer several advantages over graphite plates, including lower cost, enhanced mechanical strength, and easier fabrication. These plates can accommodate more intricate geometries, which significantly improves the power-to-volume ratio of fuel cells. Nevertheless, corrosion continues to pose a significant challenge, compromising both the durability and operational performance of the system. While protective coatings can mitigate corrosion, further innovations are necessary to achieve long-term stability. This study introduces a novel hybrid nanocomposite coating comprising graphene nanoplatelets (GNPs), platinum (Pt), and alumina (Al₂O₃), developed through nanofluid pool boiling to enhance corrosion resistance. Graphene-based nanofluids exhibit remarkable thermal conductivity and heat transfer capabilities; however, their inherent hydrophobicity necessitates surface modification for stable dispersion. In the present study, graphene nanoplatelet–platinum (GNP–Pt) nanocomposites were synthesized via acid functionalization followed by platinum deposition. These nanocomposites were subsequently utilized to formulate GNP–Pt–Al₂O₃/water hybrid nanofluids. The thermophysical properties of the prepared nanofluids—including thermal conductivity, dynamic viscosity, and boiling heat transfer performance—were comprehensively investigated. Through pool boiling tests with heated copper surfaces, enhancement of CHF was measured at 110% and enhancement in HTC was measured at 230%, as compared to the smooth surface. The incorporation of GNP, Pt, and Al₂O₃ improved the thermal properties, mechanical characteristic, and chemical stability by the enhanced thermal conductivity and structural strength, enhanced corrosion resistance, and increased surface hardness, respectively. Overall, these observations suggest the practicality of using such hybrid nanofluids in enhancing thermal management systems for PEMFCs as well as the innovative microelectronic cooling systems.